Prediction of SO2 Concentration Based on Fuzzy Time Series and Support Vector Machine
- DOI
- 10.2991/icsnce-16.2016.121How to use a DOI?
- Keywords
- SO2 concentrations; Support vector machine; Fuzzy time series; Prediction model
- Abstract
The existing prediction methods for SO2 concentration mainly have the disadvantages such as no unified sources and influences, sensitive to small sample, easy to fall into local optimum etc. In order to solve these problems, a method for SO2 concentration prediction is proposed, based on fuzzy time series and support vector machine (SVM). The method takes the seasonal variation of SO2 concentration as the basis, takes the four seasons as the time series, and takes the 24 hours as the graining window width, then extract the characteristic values of the original sample data through the Gaussian kernel function for SVM training, and optimize model parameters by k-fold cross validation method combined with the grid division. Finally, a SO2 concentrations prediction model is established by using one-hour average SO2 concentrations as sample data, and the calculation process is realized by using LIBSVM tool. The results show that the prediction method of SO2 concentration based on fuzzy time series and SVM is not restricted by the machine rational theory, can solve small-sample learning problems, and has good nonlinear fitting effect.
- Copyright
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Linliang Zhang AU - Zhaoxia Li AU - Yuejun Ma AU - Pengcheng Yue PY - 2016/07 DA - 2016/07 TI - Prediction of SO2 Concentration Based on Fuzzy Time Series and Support Vector Machine BT - Proceedings of the 2016 International Conference on Sensor Network and Computer Engineering PB - Atlantis Press SP - 623 EP - 628 SN - 2352-5401 UR - https://doi.org/10.2991/icsnce-16.2016.121 DO - 10.2991/icsnce-16.2016.121 ID - Zhang2016/07 ER -